assumptionChecking

knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)

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## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(janitor)

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(dplyr)
library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(leaps)
library(ggfortify)

data = read.table("winequality-red.csv", sep=";", header=T)

1 ALCOHOL VS QUALITY

1.1 Linearity

lm1 = lm(quality ~ alcohol, data = data)
par(cex = 0.5)
plot(quality ~ alcohol, data = data)
abline(lm1, lwd = 3, col = 'red')

1.2 Residuals and Normalitys

autoplot(lm1, which = 1:2)

2 SULFATES VS QUALITY

# SULPHATES LINEARITY
lm1 = lm(quality ~ sulphates, data = data)
par(cex = 0.5)
plot(quality ~ sulphates, data = data)
abline(lm1, lwd = 3, col = 'red')

2.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

3 VOLATILE ACIDITY VS QUALITY

# VOLATILE ACIDITY LINEARITY
lm1 = lm(quality ~ volatile.acidity, data = data)
par(cex = 0.5)
plot(quality ~ volatile.acidity, data = data)
abline(lm1, lwd = 3, col = 'red')

3.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

4 TOTAL SULFUR DIOXIDE VS QUALITY

# TOTAL SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ total.sulfur.dioxide, data = data)
par(cex = 0.5)
plot(quality ~ total.sulfur.dioxide, data = data)
abline(lm1, lwd = 3, col = 'red')

4.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

5 FREE SULFUR DIOXIDE VS QUALITY

# FREE SULFUR DIOXIDE LINEARITY
lm1 = lm(quality ~ free.sulfur.dioxide, data = data)
par(cex = 0.5)
plot(quality ~ free.sulfur.dioxide, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

6 CHLORIDES VS QUALITY

# CHLORIDES LINEARITY
lm1 = lm(quality ~ chlorides, data = data)
par(cex = 0.5)
plot(quality ~ chlorides, data = data)
abline(lm1, lwd = 3, col = 'red')

6.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

7 PH VS QUALITY

# pH LINEARITY
lm1 = lm(quality ~ pH, data = data)
par(cex = 0.5)
plot(quality ~ pH, data = data)
abline(lm1, lwd = 3, col = 'red')

7.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

8 CITRIC ACID VS QUALITY

# citric.acid LINEARITY
lm1 = lm(quality ~ citric.acid, data = data)
par(cex = 0.5)
plot(quality ~ citric.acid, data = data)
abline(lm1, lwd = 3, col = 'red')

8.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

9 RESIDUAL SUGAR VS QUALITY

# residual.sugar LINEARITY
lm1 = lm(quality ~ residual.sugar, data = data)
par(cex = 0.5)
plot(quality ~ residual.sugar, data = data)
abline(lm1, lwd = 3, col = 'red')

9.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)

10 FIXED.ACIDITY VS QUALITY

# fixed.acidity LINEARITY
lm1 = lm(quality ~ fixed.acidity, data = data)
par(cex = 0.5)
plot(quality ~ fixed.acidity, data = data)
abline(lm1, lwd = 3, col = 'red')

## Residuals and Normalitys

autoplot(lm1, which = 1:2)

11 DENSITY VS QUALITY

# density LINEARITY
lm1 = lm(quality ~ density, data = data)
par(cex = 0.5)
plot(quality ~ density, data = data)
abline(lm1, lwd = 3, col = 'red')

11.1 Residuals and Normalitys

autoplot(lm1, which = 1:2)